SYSTEM AND METHOD FOR RECOGNIZING VERTICALLY ORIENTED ALPHANUMERIC TEXT IN IMAGES

1 . A system for recognizing vertically oriented alphanumeric text in images, the system comprising:

a processor configured to:

receive one or more images comprising vertically oriented alphanumeric text with respect to a ground plane;

detect one or more regions-of-interest in each image of the one or more images, via a trained text detector, the one or more regions-of-interest comprising the vertically oriented alphanumeric text;

execute a cropping of the detected one or more regions-of-interest encompassing corresponding vertically oriented alphanumeric text from each image of the one or more images to obtain one or more text crop portions;

rotate the one or more text crop portions by 90 degrees to obtain one or more orthogonally rotated text crop portions;

execute a trained ensemble of two different text recognition models on each of the obtained one or more text crop portions and the one or more orthogonally rotated text crop portions;

generate a set of candidate recognized text strings based on the executed trained ensemble of two different text recognition models; and

determine a final recognized text string from the generated set of candidate recognized text strings based on a defined camera based parameter and a text-character frequency parameter.

2 . The system of claim 1 , wherein the defined camera based parameter is indicative of a number of camera views from which the detected region-of-interest is captured, and wherein a candidate recognized text string from the generated set of candidate recognized text strings identified in the same detected region-of-interest by two or more cameras is given a higher priority.

3 . The system of claim 1 , wherein the text-character frequency parameter comprises, for each candidate text string, a count of how frequently the text string is output by the trained ensemble across the one or more images.

4 . The system of claim 1 , wherein the trained ensemble of two different text recognition models comprises a first text recognition model trained on a first training dataset comprising a plurality of vertically oriented synthetic and real-world alphanumeric text samples.

5 . The system of claim 1 , wherein the trained ensemble of two different text recognition models comprises a second text recognition model trained on a second training dataset comprising a plurality of rotated vertically oriented synthetic and real-world alphanumeric text samples.

6 . The system of claim 1 , wherein the final recognized text string is identified as a vehicle trailer number based on matching a predefined character format and set.

7 . The system of claim 6 , wherein the processor is further configured to: query a database using the identified vehicle trailer number to retrieve associated shipment information; and trigger one or more supply chain management workflows based on the retrieved shipment information.

8 . The system of claim 1 , wherein the processor is configured to train the trained text detector using a third training dataset generated by overlaying alphanumeric characters on backgrounds scraped from various trailers and applying realistic font styles commonly found in a trucking industry.

9 . The system of claim 8 , wherein the generated third training dataset further comprises synthetic images generated by: sampling background images from the real images of vehicle trailers; rendering vertically oriented text strings onto the sampled background images using fonts commonly found on vehicle trailers; and applying one or more data augmentation techniques to the rendered text strings.

10 . The system of claim 9 , wherein the one or more data augmentation techniques comprise one or more of: skewing, perspective transforming, adjusting character spacing, adding noise patterns, or applying spatial dropout.

11 . A method, comprising:

receiving, by a processor, one or more images comprising vertically oriented alphanumeric text with respect to a ground plane;

detecting, by the processor, one or more regions-of-interest in each image of the one or more images, via a trained text detector, the one or more regions-of-interest comprising the vertically oriented alphanumeric text;

executing, by the processor, a cropping of the detected one or more regions-of-interest encompassing corresponding vertically oriented alphanumeric text from each image of the one or more images to obtain one or more text crop portions;

rotating, by the processor, the one or more text crop portions by 90 degrees to obtain one or more orthogonally rotated text crop portions;

executing, by the processor, a trained ensemble of two different text recognition models on each of the obtained one or more text crop portions and the one or more orthogonally rotated text crop portions;

generating, by the processor, a set of candidate recognized text strings based on the executed trained ensemble of two different text recognition models; and

determining, by the processor, a final recognized text string from the generated set of candidate recognized text strings based on a defined camera based parameter and a text-character frequency parameter.

12 . The method of claim 11 , wherein the defined camera based parameter is indicative of a number of camera views from which the detected region-of-interest is captured, and wherein a candidate recognized text string from the generated set of candidate recognized text strings identified in the same detected region-of-interest by two or more cameras is given a higher priority.

13 . The method of claim 11 , wherein the text-character frequency parameter comprises, for each candidate text string, a count of how frequently the text string is output by the trained ensemble across the one or more images.

14 . The method of claim 11 , wherein the trained ensemble of two different text recognition models comprises a first text recognition model trained on a first training dataset comprising a plurality of vertically oriented synthetic and real-world alphanumeric text samples.

15 . The method of claim 11 , wherein the trained ensemble of two different text recognition models comprises a second text recognition model trained on a second training dataset comprising a plurality of rotated vertically oriented synthetic and real-world alphanumeric text samples.

16 . The method of claim 11 , wherein the final recognized text string is identified as a vehicle trailer number based on matching a predefined character format and set.

17 . The method of claim 16 , wherein the method further comprises querying a database using the identified vehicle trailer number to retrieve associated shipment information; and trigger one or more supply chain management workflows based on the retrieved shipment information.

18 . The method of claim 11 , wherein the method further comprises training the trained text detector using a third training dataset generated by overlaying alphanumeric characters on backgrounds scraped from various trailers and applying realistic font styles commonly found in a trucking industry.

19 . The method of claim 18 , wherein the generated third training dataset further comprises synthetic images generated by: sampling background images from the real images of vehicle trailers; rendering vertically oriented text strings onto the sampled background images using fonts commonly found on vehicle trailers; and applying one or more data augmentation techniques to the rendered text strings.

20 . The method of claim 19 , wherein the one or more data augmentation techniques comprise one or more of: skewing, perspective transforming, adjusting character spacing, adding noise patterns, or applying spatial dropout.